Federated Learning Client Selection Scheme Based on Time-varying Computing Resources
Federated learning(FL)is an emerging paradigm for distributed machine learning,whose core idea is that user devices train their models locally in a distributed manner and do not need to upload raw data,but only upload the trained model to the server for model aggregation.Most of the existing studies ignore that the computing resources of devices change temporally with the usage patterns of users,which can affect the training of FL.In this paper,we model time-varying computing resources for he-terogeneous devices using an auto regressive model and propose a client selection algorithm.We first formulate the optimization problem of minimizing the average training time of each round of FL under the long-term training time constraint,then transform it using Lyapunov optimization theory,and finally solve it to obtain the client selection algorithm.Experimental results show that compared with the baseline algorithms,the proposed algorithm can reduce the training time of FL and the average waiting time of the devices while basically remaining the quality of model.
Federated learningClient selectionTime-varying computing resourcesUnbalanced data
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